[PYTHON/LANGCHAIN] InMemoryVectorStore 클래스 : as_retriever 메소드를 사용해 VectorStoreRetriever 객체 만들기

■ InMemoryVectorStore 클래스의 as_retriever 메소드를 사용해 VectorStoreRetriever 객체를 만드는 방법을 보여준다.

▶ main.py


from langchain_core.documents    import Document
from langchain_openai            import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore

documentList = [
    Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),
    Document(page_content = "Cats are independent pets that often enjoy their own space."         )
]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(
    documentList,
    embedding = openAIEmbeddings
)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(
    search_type   = "similarity",
    search_kwargs = {"k" : 1}
)

from langchain_core.documents import Document

from langchain_openai import OpenAIEmbeddings

from langchain_core.vectorstores import InMemoryVectorStore

documentList = [

Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),

Document(page_content = "Cats are independent pets that often enjoy their own space." )

]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(

documentList,

embedding = openAIEmbeddings

)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(

search_type = "similarity",

search_kwargs = {"k" : 1}

)

※ pip install langchain-openai 명령을 실행했다.